主要思想：
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| GAPfm: Optimal Top-N Recommendations for Graded Relevance Domains | |||||||||||
| 文献作者 | Yue Shi; Alexandros Karatzoglou; Linas Baltrunas | ||||||||||
| 文献发表年限 | 2013 | ||||||||||
| 文献关键字 | |||||||||||
| Collaborative filtering, graded average precision, latent factor model, recommender systems, top-n recommendation, ranking | |||||||||||
| 摘要描述 | |||||||||||
| Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. Current ranking techniques are based on one of two sub-optimal approaches: either they optimize for a binary metric such as Average Precision, which discards information on relevance levels, or they optimize for Normalized Discounted Cumulative Gain (NDCG), which ignores the dependence of an item’s contribution on the relevance of more highly ranked items. We address the short-comings of existing approaches by proposing GAPfm, the Graded Average Precision factor model, which is a latent factor model for top-N recommendation in domains with graded relevance data. The model optimizes the Graded Average Precision metric that has been proposed recently for assessing the quality of ranked results lists for graded relevance. GAPfm’s advantages are twofold: it maintains full information about graded relevance and also addresses the limitations of models that optimize NDCG. Experimental results show that GAPfm achieves substantial improvements on the top-N recommendation task, compared to several state-of-the-art approaches. | |||||||||||
